Retinal development is tightly controlled through a variety of regulatory mechanisms such as transcriptional regulation, alternative splicing, and microRNAs. Perturbation of the retinal regulatory network can lead to various retinal diseases. High-throughput technologies (e.g., microarray or next generation sequencing) have identified many genes that are likely to play a role in retinal gene regulation. However, these genes were often identified individually and no information was provided about their interactions between each other. Previous studies have suggested that it is likely that molecular circuits carry out biological functions and define the retinal development. Identification of the key molecular circuits in the regulatory network can help to understand the molecular basis of retinal diseases. The objectives of the present application are to identify network motifs (i.e., molecular circuits) that define the retinal development and how they are perturbed in retinal diseases. The network motifs will consist of transcription factors and microRNAs, two types of important regulators in gene regulatory network. The rationale for the proposed research is that. once we have determined the network motifs in retinal regulatory network, we will be able to better understand the molecular mechanisms of retinal disease, ultimately resulting in new and innovative therapeutics for the prevention and treatment of a variety of retinal diseases. In addition, this study will allow us to understand the crosstalk between transcription factors and microRNAs in general. We have two specific aims: 1) Identification of molecular circuits that regulate retinal development and homeostasis; and 2) Identification of molecular circuits that regulate retinal degeneration and diseases. For this study, we will integrate several large-scale datasets, including chromatin immunoprecipitation coupled with microarray (ChIP-chip) and with sequencing (ChIP-seq) and gene expression profiling in different conditions. We will then identify the network motifs in both normal and diseased retinal regulatory network. Our approach is innovative because by integrating orthogonal datasets relating to retinal regulation, we will be able to gain the maximal and key information from these massive datasets. This proposed research is significant because it is the first effort to systematically identify network motifs in various retinal conditions. It will also shift the paradigm from individual factor-based to molecular circuit-based analysis of retinal regulation. We expect the results will ultimately advance our understanding of retinal disease mechanisms and the development of novel therapeutics.